Performance comparison of different control strategies for heat exchanger networks

Open access


In this article, the dynamic responses of heat exchanger networks to disturbance and setpoint change were studied. Various control strategies, including: proportional integral, model predictive control, passivity approach, and passivity-based model predictive control were used to monitor all outlet temperatures. The performance of controllers was analyzed through two procedures: 1) inducing a ±5% step disturbance in the supply temperature, or 2) tracking a ±5°C target temperature. The performance criteria used to evaluate these various control modes was settling time and percentage overshoot. According to the results, the passivity-based model predictive controllers produced the best performance to reject the disturbance and the model predictive control proved to be the best controller to track the setpoint. Whereas, the ensuing performance results of both the PI and passivity controllers were discovered to be only acceptable.

1. Westphalen, D.L., Young, B.R. & Svrcek, W.Y. (2003). A Controllability Index for Heat Exchanger Networks. Ind. Eng. Chem. Res. 42(20), 4659–4667. DOI: 10.1021/ie020893z.

2. Seborg, D.E., Edgar, T.F., Mellichamp, D.A. & Doyle III, F.J. (2011). Process dynamics and control (3rd ed.). New Jersey, USA: John Wiley & Sons, Inc.

3. Grüne, L. & Pannek, J. (2011). Nonlinear Model Predictive Control: Theory and Algorithms. London United Kingdom: Springer-Verlag London Limited.

4. Bakosova, M. & Oravec, J. (2014). Robust model predictive control for heat exchanger network. Appl. Therm. Eng. 73, 924–930. DOI: 10.1016/j.applthermaleng.2014.08.023.

5. Rene, A.S. (2016). Model Predictive Control of District Heating Systems. Master dissertation, Norwegian University of Science and Technology, Trondheim, Norway.

6. Heidarinejad, M., Liu, J. & Christofides, P.D. (2011). Lyapunov-based economic model predictive control of nonlinear systems. In American Control Conference, 29 June – 1 July 2011 (pp. 5195–5200). San Francisco, CA, USA.

7. Liu, J., Munoz de la Pena, D., Christofides, P.D. & Davis, J.F. (2009). Lyapunov-based model predictive control of nonlinear systems subject to time-varying measurement delays. Int. J. Adapt. Control Signal Process. 23(8), 788–807. DOI: 10.1002/acs.1085.

8. Pukdeboon, C. (2011). A Review of Fundamentals of Lyapunov Theory. J. Appl. Sci. 10(2), 55–61.

9. Bao, J., Wan, F.Y., Lee, P.L. & Zhou, W.B. (1996). New frequency-domain phase-related properties of MIMO LTI passive systems and robust controller synthesis. In 13th IFAC World Congress, 30 June – 5 July 1996 (pp. 405–410). San Francisco, CA, USA.

10. Bao, J., Lee, P.L., Wan, F.Y. & Zhou, W.B. (2000). A New Approach to Decentralized Process Control Using Passivity and Sector Stability Conditions. Chem. Eng. Commun. 182(1), 213–237. DOI: 10.1080/00986440008912835.

11. Bao, J., Zhang, W.Z. & Lee, P.L. (2002). Passivity-Based Decentralized Failure-Tolerant Control. Ind. Eng. Chem. Res. 41(23), 5702–5715. DOI: 10.1021/ie0201314.

12. Zhang, W.Z., Bao, J. & Lee, P.L. (2002). Decentralized Unconditional Stability Conditions Based on the Passivity Theorem for Multi-loop Control Systems. Ind. Eng. Chem. Res. 41(6), 1569–1578. DOI: 10.1021/ie001037v.

13. Raff, T., Ebenbauer, C. & Allgower, P. (2007). Nonlinear Model Predictive Control: A Passivity-based Approach. In I.R. Findeisen, F. Allgower & L.T. Biegler (Eds.), Assessment and Future Directions of Nonlinear Model Predictive Control (pp. 151–162). Germany: Springer Berlin Heidelberg.

14. Bao, J., Zhang, W.Z. & Lee, P.L. (2000). A Passivity-based Approach to Multi-loop PI Controller Tuning. In 6th International Conference on Control, Automation, Robotics and Vision, 5–8 December 2000 (paper 178). Singapore.

15. Bao, J. & Lee, P.L. (2007). Process Control: The Passive Systems Approach. United Kingdom: Springer-Verlag London Limited.

16. Pariyani, A., Gupta, A. & Ghosh, P. (2006). Design of heat exchanger networks using randomized algorithm. Comput. Chem. Eng. 30(6–7), 1046–1053. DOI: 10.1016/j.compchemeng.2006.01.005.

Polish Journal of Chemical Technology

The Journal of West Pomeranian University of Technology, Szczecin

Journal Information

IMPACT FACTOR 2017: 0.55
5-year IMPACT FACTOR: 0.655

CiteScore 2016: 0.76

SCImago Journal Rank (SJR) 2016: 0.262
Source Normalized Impact per Paper (SNIP) 2016: 0.462


All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 109 109 54
PDF Downloads 28 28 16